In my experiment, I want to test whether dogs are more likely to head directly to a goal. The data is in the form so that $0$ (degrees) is a heading to the goal, $-90^\circ$ is a heading to the left and $90^\circ$ would be heading to the right.
Experimental group (headings in degrees): 3,0,-7,-10,10,-1,13,15,-3,-2,6,6,5 Control group (headings in degrees): -40,124,178,137,55,58,139,25,8,26,132,179,152
I want to test the hypothesis that in the experimental condition, the dogs are more accurate (closer to $0$). I thought a good way of testing accuracy was to have a null hypothesis that "variances will be equal in both conditions", although technically the variances in the experimental condition could be very small (so the dogs all head in the same position) but in the wrong direction (mean heading could be $50^\circ$, for example).
Either way, if I did want to test the hypothesis, I thought of doing a Brown-Forysthe test which is essentially (if I am right) an ANOVA which tests if the absolute deviations from the median differ. However, the variances of the groups differ HUGELY since the dogs in the experimental condition were more consistent. Would it therefore be more appropriate to use a unequal variances t-test instead of an ANOVA?